Poisson autoregressive process modeling via the penalized conditional maximum likelihood procedure

被引：0
|
作者：
Xinyang Wang
Dehui Wang
Haixiang Zhang
机构：
[1] Mathematics School of Jilin University,Center for Applied Mathematics
[2] Tianjin University,undefined
来源：
Statistical Papers | 2020年 / 61卷
关键词：
Integer-valued time series; Penalty function; Poisson autoregressive; Oracle properties;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we consider the penalized estimation procedure for Poisson autoregressive model with sparse parameter structure. We study the theoretical properties of penalized conditional maximum likelihood (PCML) with several different penalties. We show that the penalized estimators perform as well as the true model was known. We establish the oracle properties of PCML estimators. Some simulation studies are conducted to verify the proposed procedure. A real data example is also provided.
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页码：245 / 260
页数：15
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